bitcoin_price_prediction VS Anomaly_Detection_Tuto

Compare bitcoin_price_prediction vs Anomaly_Detection_Tuto and see what are their differences.

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bitcoin_price_prediction Anomaly_Detection_Tuto
1 2
10 182
- -
0.0 0.0
about 3 years ago about 3 years ago
Jupyter Notebook Jupyter Notebook
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

bitcoin_price_prediction

Posts with mentions or reviews of bitcoin_price_prediction. We have used some of these posts to build our list of alternatives and similar projects.

Anomaly_Detection_Tuto

Posts with mentions or reviews of Anomaly_Detection_Tuto. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing bitcoin_price_prediction and Anomaly_Detection_Tuto you can also consider the following projects:

mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.

alibi-detect - Algorithms for outlier, adversarial and drift detection

NYU-DLSP20 - NYU Deep Learning Spring 2020

tsfresh - Automatic extraction of relevant features from time series:

TrainInvaders - 👾 Jupyter Notebook + Space Invaders!?

mix_merkletree - Usefull Merkle Tree Example

Python-Bible - Resourceful Python Collection

Animender - An AI that recommends anime based on personal history.

it-salary-analysis - 💰 Analysis of Salaries in IT Roles: DevOps, Cyber Security, and AI

biggestwar_ai

peacasso - UI interface for experimenting with multimodal (text, image) models (stable diffusion).

Diabetes-Prediction-Using-SVM - In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.